Laser & Optoelectronics Progress, Volume. 62, Issue 16, 1612005(2025)

Power Line Extraction Algorithm Based on Improved-Canny and Sag Measurement Application

Xingzhi Ren, Yu Fang*, Diqing Fan, Hao Yang, Minghong Wang, and Qiangbao Ouyang
Author Affiliations
  • School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
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    Figures & Tables(19)
    Sag measurement model
    Sag measurement device
    Power line image acquisition scenario
    Partial acquired power line images
    Flowchart of power line extraction algorithm
    Flowchart of image preprocessing
    Power line edge detection
    Principle diagram of HT
    Power line extraction
    Edge detection results of different algorithms. (a) Single conductor; (b) bundled conductors; (c) bundled conductors with spacer bars against a blue sky background; (d) bundled conductors with spacer bars against a cloud background
    Power line extraction results. (a) Single conductor; (b) bundled conductors; (c) bundled conductors with spacer bars against a blue sky background; (d) bundled conductors with spacer bars against a cloud background
    Field experiment power line images. (a) Single conductor; (b) bundled conductors
    Automatic process for sag measurement
    Comparison of error rates for sag
    • Table 1. PSNR values ​​of different algorithms

      View table

      Table 1. PSNR values ​​of different algorithms

      ImageCannySobelPrewittImproved-Canny
      Fig. 10(a)5.025.574.995.91
      Fig. 10(b)3.844.273.774.34
      Fig. 10(c)6.346.876.246.97
      Fig. 10(d)5.195.685.125.82
    • Table 2. Total number of edge points extracted by different algorithms

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      Table 2. Total number of edge points extracted by different algorithms

      ImageCannySobelPrewittImproved-Canny
      Fig. 10(a)13290172017271883
      Fig. 10(b)6279589459005967
      Fig. 10(c)10545505550815313
      Fig. 10(d)10570538554125750
    • Table 3. Edge image statistics of different algorithms

      View table

      Table 3. Edge image statistics of different algorithms

      ImageC/AC/B
      CannySobelPrewittImproved-CannyCannySobelPrewittImproved-Canny
      Fig. 10(a)0.0290.0430.0490.0020.1890.2030.2260.018
      Fig. 10(b)0.0110.0200.0240.0030.0910.1270.1510.024
      Fig. 10(c)0.0340.0090.0130.0030.1770.0290.0400.023
      Fig. 10(d)0.0250.0050.0090.0030.1710.0250.0480.028
    • Table 4. Power line extraction accuracies

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      Table 4. Power line extraction accuracies

      Image typeImage quantityCorrect quantityAccuracy /%
      Single conductor25123995.219
      Bundled conductors20819995.673
      Total45943895.425
    • Table 5. Comparison of data collected using different methods

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      Table 5. Comparison of data collected using different methods

      TestMeasuring time /minSag /mError rate of sag /%
      ManualAutomaticManualAutomaticManualAutomatic
      Average26.36116.02015.9315.712.121.46
      125.71616.51716.3315.954.682.24
      226.07316.33315.8415.801.541.28
      326.15415.66715.7215.830.771.47
      425.43516.01715.8815.431.791.09
      527.43615.90015.7115.330.711.73
      626.45816.06715.8415.701.540.64
      727.62317.83316.0415.882.821.79
      825.92615.53716.1415.823.461.41
      926.06514.96315.7915.911.221.99
      1026.72115.36216.0215.452.690.96
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    Xingzhi Ren, Yu Fang, Diqing Fan, Hao Yang, Minghong Wang, Qiangbao Ouyang. Power Line Extraction Algorithm Based on Improved-Canny and Sag Measurement Application[J]. Laser & Optoelectronics Progress, 2025, 62(16): 1612005

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Jan. 23, 2025

    Accepted: Mar. 28, 2025

    Published Online: Jul. 24, 2025

    The Author Email: Yu Fang (fangyu_hit@126.com)

    DOI:10.3788/LOP250574

    CSTR:32186.14.LOP250574

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